To deal with the discrete and continuous action space, a parameterized deep Q-network framework was proposed in [1]} to handle the hybrid action space based on the definition of hierarchical structure on the actions rather than using continuous actions discretization. Furthermore, the method in [1]} was adopted in [3]} to solve the power and subchannel allocation of the nonorthogonal multiple access system by proposing a joint DRL algorithm. Although the proposed joint algorithm in [3]} is able to efficiently handle the discrete-continuous hybrid action space in wireless communications systems, this algorithm will cause a high overhead and inaccurate resources allocation for V2X communications because it is designed based on a centralized setup in which the BS acts as the central controller to allocate resources for each user. Hence, it is necessary to design a distributed DRL algorithm, which is suitable for V2X communications to efficiently handle decision-making on the combination of power and spectrum resources. Following the idea in [1]} and [3]}, we propose to use DQN to handle discrete sub-band assignment and deep deterministic policy gradient (DDPG) to handle continuous power allocation in order to overcome the first challenge caused by the hybrid action space of V2X communications. The ability of meta-learning in solving mismatch issues has been proven in computer vision [7]} and wireless communications [8]}, [9]}. Hence, we propose to incorporate meta-learning into DRL to overcome the second challenge caused by the continuously changing environment in V2X communications. To be specific, we first propose a distributed DRL-based algorithm to achieve the optimal solution of sub-band assignment and power allocation for V2X communications in which V2V links need to share the sub-band resources with V2I links. Then, we propose a meta-based DRL algorithm to improve the adaptation ability in the dynamic environment. We summarize the main contributions of this paper as follows:
